Pub Date : 1900-01-01DOI: 10.1109/CONFLUENCE.2016.7508134
Chitresh Verma, R. Pandey
Big Data is a large dataset displaying the features of volume, velocity and variety in an OR relationship. Big Data as a large dataset is of no significance if it cannot be exposed to strategic analysis and utilization. There are many software and hardware solutions available in the technological landscape that enable capturing, storing and subsequently analysis of Big Data. Hadoop and its associated technological solution is one of them. Hadoop is the software framework for computing large amount of data. It is made up of four main modules. These modules are Hadoop Common, Hadoop Distributed File System (HDFS), Hadoop YARN, and Hadoop MapReduce. Hadoop MapReduce divides large problem into smaller sub problems under the control of JobTracker. This paper suggests a Big Data representation for grade analytics in an educational context. The study and the experiments can be implemented on R or AWS the cloud infrastructure provided by Amazon.
{"title":"Big Data representation for grade analysis through Hadoop framework","authors":"Chitresh Verma, R. Pandey","doi":"10.1109/CONFLUENCE.2016.7508134","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2016.7508134","url":null,"abstract":"Big Data is a large dataset displaying the features of volume, velocity and variety in an OR relationship. Big Data as a large dataset is of no significance if it cannot be exposed to strategic analysis and utilization. There are many software and hardware solutions available in the technological landscape that enable capturing, storing and subsequently analysis of Big Data. Hadoop and its associated technological solution is one of them. Hadoop is the software framework for computing large amount of data. It is made up of four main modules. These modules are Hadoop Common, Hadoop Distributed File System (HDFS), Hadoop YARN, and Hadoop MapReduce. Hadoop MapReduce divides large problem into smaller sub problems under the control of JobTracker. This paper suggests a Big Data representation for grade analytics in an educational context. The study and the experiments can be implemented on R or AWS the cloud infrastructure provided by Amazon.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132767832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CONFLUENCE.2016.7508132
Veenita Kunwar, Khushboo Chandel, A. Sabitha, Abhay Bansal
Data mining has been a current trend for attaining diagnostic results. Huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from massive dataset, categorizing valid and unique patterns in data. There are many data mining techniques like clustering, classification, association analysis, regression etc. The objective of our paper is to predict Chronic Kidney Disease(CKD) using classification techniques like Naive Bayes and Artificial Neural Network(ANN). The experimental results implemented in Rapidminer tool show that Naive Bayes produce more accurate results than Artificial Neural Network.
{"title":"Chronic Kidney Disease analysis using data mining classification techniques","authors":"Veenita Kunwar, Khushboo Chandel, A. Sabitha, Abhay Bansal","doi":"10.1109/CONFLUENCE.2016.7508132","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2016.7508132","url":null,"abstract":"Data mining has been a current trend for attaining diagnostic results. Huge amount of unmined data is collected by the healthcare industry in order to discover hidden information for effective diagnosis and decision making. Data mining is the process of extracting hidden information from massive dataset, categorizing valid and unique patterns in data. There are many data mining techniques like clustering, classification, association analysis, regression etc. The objective of our paper is to predict Chronic Kidney Disease(CKD) using classification techniques like Naive Bayes and Artificial Neural Network(ANN). The experimental results implemented in Rapidminer tool show that Naive Bayes produce more accurate results than Artificial Neural Network.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"163 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129230450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CONFLUENCE.2016.7508171
Harshita Gupta, Divya Gupta
Automatic speech recognition (ASR) has been under the scrutiny of researchers for many years. Speech Recognition System is the ability to listen what we speak, interpreter and perform actions according to spoken information. After so many detailed study and optimization of ASR and various techniques of features extraction, accuracy of the system is still a big challenge. The selection of feature extraction techniques is completely based on the area of study. In this paper, a detailed theory about features extraction techniques like LPC and LPCC is examined. The goal of this paper is to study the comparative analysis of features extraction techniques like LPC and LPCC.
{"title":"LPC and LPCC method of feature extraction in Speech Recognition System","authors":"Harshita Gupta, Divya Gupta","doi":"10.1109/CONFLUENCE.2016.7508171","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2016.7508171","url":null,"abstract":"Automatic speech recognition (ASR) has been under the scrutiny of researchers for many years. Speech Recognition System is the ability to listen what we speak, interpreter and perform actions according to spoken information. After so many detailed study and optimization of ASR and various techniques of features extraction, accuracy of the system is still a big challenge. The selection of feature extraction techniques is completely based on the area of study. In this paper, a detailed theory about features extraction techniques like LPC and LPCC is examined. The goal of this paper is to study the comparative analysis of features extraction techniques like LPC and LPCC.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115016381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CONFLUENCE.2016.7508199
A. Singhal, D. Pandey, Renuka Nagpal, D. Mehrotra
A web document of an educational web site is a document which caters updated and latest information to the user within precise time that defines the informativeness of a web document. While navigating a web document user expects that the facts which are cited in a web document must be Complete, Current, Accurate, and Reliable. On the basis of these factors in the ensuing paper we took a survey of 6 educational web documents which was conducted by 100 students per web document in which we have enforced Factor Analysis and assertive tests which assures the adequacy and significance of the sample using SPSS tool to find the principal components of these factors. On the basis of eigenvalue above 1 of those 4 factors in the ensuing paper we have prioritized the factors on the basis of which equipped information should be commenced in a sequenced and prioritized demeanour in a web document.
{"title":"Measuring informativeness of a web document","authors":"A. Singhal, D. Pandey, Renuka Nagpal, D. Mehrotra","doi":"10.1109/CONFLUENCE.2016.7508199","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2016.7508199","url":null,"abstract":"A web document of an educational web site is a document which caters updated and latest information to the user within precise time that defines the informativeness of a web document. While navigating a web document user expects that the facts which are cited in a web document must be Complete, Current, Accurate, and Reliable. On the basis of these factors in the ensuing paper we took a survey of 6 educational web documents which was conducted by 100 students per web document in which we have enforced Factor Analysis and assertive tests which assures the adequacy and significance of the sample using SPSS tool to find the principal components of these factors. On the basis of eigenvalue above 1 of those 4 factors in the ensuing paper we have prioritized the factors on the basis of which equipped information should be commenced in a sequenced and prioritized demeanour in a web document.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"31 16-17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120929986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CONFLUENCE.2016.7508114
Anupam Sharma, Bhupender Singh, Rishi Kumar
This paper shows various services provided in cloud computing and use of these services directly via Operating System. With the increasing popularity of cloud computing concept and its availability encourages more and more organization to switch their environment to cloud based environment. This paper deals with the concept of using all kinds of software requirements through cloud services by an operating system with mere hardware requirement and thus remotely accessing all those applications installed on the cloud, also referred as “Cloud Driving” in the paper. This approach can drastically minimize the cost of hardware and also with the increasing use of high speed internet; this concept seems more logical and efficient. The operating system used for implementing this concept is of utmost importance because here it's the OS which directly interact with services provided by the cloud. Linux is used here because of its stability and customizability. There are few more reasons for choosing Linux as it is lightweight and more secure.
{"title":"Versatile Web Based thin client OS using Cloud services by using concept of Cloud Driving","authors":"Anupam Sharma, Bhupender Singh, Rishi Kumar","doi":"10.1109/CONFLUENCE.2016.7508114","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2016.7508114","url":null,"abstract":"This paper shows various services provided in cloud computing and use of these services directly via Operating System. With the increasing popularity of cloud computing concept and its availability encourages more and more organization to switch their environment to cloud based environment. This paper deals with the concept of using all kinds of software requirements through cloud services by an operating system with mere hardware requirement and thus remotely accessing all those applications installed on the cloud, also referred as “Cloud Driving” in the paper. This approach can drastically minimize the cost of hardware and also with the increasing use of high speed internet; this concept seems more logical and efficient. The operating system used for implementing this concept is of utmost importance because here it's the OS which directly interact with services provided by the cloud. Linux is used here because of its stability and customizability. There are few more reasons for choosing Linux as it is lightweight and more secure.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115852069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CONFLUENCE.2016.7508108
G. Gandhimathi, N. Thinakaran
As per the report of predictions cloud study 2014 and Cisco Global cloud Index 2013-2018, the public cloud service market with IaaS, PaaS, SaaS, cloud management and security services conquer significant from S76.9B in 2011 to $210B in 2016. Multicast conferencing plays a vital role in e-education and mobile applications including e-business. In real time, for achieving low bit rates, narrow band speech is used in VoIP applications. Importing Artificial bandwidth extension algorithm is the key cloud usage along with AT&T speech mash up. This paper discusses the challenges while handing big Data and big Content and presents a solution to optimize the benefits provided through advances in cloud computing. This paper also discusses the challenge of ensuring data security and the solution through encryption in public cloud models.
{"title":"Encrypting the artificial bandwidth extension algorithm for multicast conferencing in cloud environment","authors":"G. Gandhimathi, N. Thinakaran","doi":"10.1109/CONFLUENCE.2016.7508108","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2016.7508108","url":null,"abstract":"As per the report of predictions cloud study 2014 and Cisco Global cloud Index 2013-2018, the public cloud service market with IaaS, PaaS, SaaS, cloud management and security services conquer significant from S76.9B in 2011 to $210B in 2016. Multicast conferencing plays a vital role in e-education and mobile applications including e-business. In real time, for achieving low bit rates, narrow band speech is used in VoIP applications. Importing Artificial bandwidth extension algorithm is the key cloud usage along with AT&T speech mash up. This paper discusses the challenges while handing big Data and big Content and presents a solution to optimize the benefits provided through advances in cloud computing. This paper also discusses the challenge of ensuring data security and the solution through encryption in public cloud models.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"259 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116221100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CONFLUENCE.2016.7508122
B. Gandhi, Tanvi Sachdev, J. Jadon, Arvind Kumar
The following paper presents a relative study of a microstrip patch antenna which is excited using the inset feed strip line and the other is the microstrip fractal antenna which is excited using the coaxial feed mechanism. The antenna prototypes were designed and simulated on HFSS. Several parameters such as gain, VSWR, return loss were obtained. The characteristics obtained for Antenna 1 that is the inset fed microstrip slot patch antenna are -12.37dB, -26.31 dB and - 11.20 dB and for Antenna 2 i.e microstrip fractal antenna are 18.50dB, -24.00 dB and -27.00 dB and the gain for the two antennas are 5.29dbi and 3.32dbi respectively. The applications include Mobile and satellite communication application, Global Positioning System applications, Radar Application, Rectenna Application etc.
{"title":"Designing and performance metrics analysis of microstrip antenna and microstrip patch fractal antenna","authors":"B. Gandhi, Tanvi Sachdev, J. Jadon, Arvind Kumar","doi":"10.1109/CONFLUENCE.2016.7508122","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2016.7508122","url":null,"abstract":"The following paper presents a relative study of a microstrip patch antenna which is excited using the inset feed strip line and the other is the microstrip fractal antenna which is excited using the coaxial feed mechanism. The antenna prototypes were designed and simulated on HFSS. Several parameters such as gain, VSWR, return loss were obtained. The characteristics obtained for Antenna 1 that is the inset fed microstrip slot patch antenna are -12.37dB, -26.31 dB and - 11.20 dB and for Antenna 2 i.e microstrip fractal antenna are 18.50dB, -24.00 dB and -27.00 dB and the gain for the two antennas are 5.29dbi and 3.32dbi respectively. The applications include Mobile and satellite communication application, Global Positioning System applications, Radar Application, Rectenna Application etc.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"195 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122524420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CONFLUENCE.2016.7508207
Udit Pant, S. Dubey
Indeed, there are no second thoughts over the supremacy of quantum computers over classical computers. But, this fact prevails more in theoretical aspects than practical. Quantum computing being highly conjugated with the quantum phenomenon has certain implications. The fact that quantum systems can be designed rather than being self-existent is a major turnout. The challenge remains to design noise-free environments for the quantum system to work capably. Major requirements for the development of qubits like critically low temperatures (for superconductivity) are not only difficult to achieve but can be economically unfriendly. However, even a tiny shift of perspective in the approach can be crucial in the development of efficient quantum systems. Due to the lack of stronghold in the subject of quantum physics, the possibility of developing quantum computers in the near future might just be very distant.
{"title":"Perspective approach in quantum computing","authors":"Udit Pant, S. Dubey","doi":"10.1109/CONFLUENCE.2016.7508207","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2016.7508207","url":null,"abstract":"Indeed, there are no second thoughts over the supremacy of quantum computers over classical computers. But, this fact prevails more in theoretical aspects than practical. Quantum computing being highly conjugated with the quantum phenomenon has certain implications. The fact that quantum systems can be designed rather than being self-existent is a major turnout. The challenge remains to design noise-free environments for the quantum system to work capably. Major requirements for the development of qubits like critically low temperatures (for superconductivity) are not only difficult to achieve but can be economically unfriendly. However, even a tiny shift of perspective in the approach can be crucial in the development of efficient quantum systems. Due to the lack of stronghold in the subject of quantum physics, the possibility of developing quantum computers in the near future might just be very distant.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"354 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122794803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CONFLUENCE.2016.7508177
Rashim Bhardwaj, P. Singh
The main motive of this review paper is to recognise the human activities in video using different posses and various types of activities done by human in video. To achieve this activity recognition author's used a different technique such as object segmentation, feature extraction and representation, Hidden markov model, bag of word approach. And some basic concepts of machine learning and algorithms such as supervised learning, clustering, Linear Discriminant analysis, Finite state automata, K-Nearest Neighbour have been used. The domain area for this analysis is surveillances, entertainment and healthcare environment. And the authors have collected the data for their analysis from various sources such as Youtube, movies, real human activities videos are collected from Railway stations, banks, hospitals, circus area specially which are under the camera notification.
{"title":"Analytical review on human activity recognition in video","authors":"Rashim Bhardwaj, P. Singh","doi":"10.1109/CONFLUENCE.2016.7508177","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2016.7508177","url":null,"abstract":"The main motive of this review paper is to recognise the human activities in video using different posses and various types of activities done by human in video. To achieve this activity recognition author's used a different technique such as object segmentation, feature extraction and representation, Hidden markov model, bag of word approach. And some basic concepts of machine learning and algorithms such as supervised learning, clustering, Linear Discriminant analysis, Finite state automata, K-Nearest Neighbour have been used. The domain area for this analysis is surveillances, entertainment and healthcare environment. And the authors have collected the data for their analysis from various sources such as Youtube, movies, real human activities videos are collected from Railway stations, banks, hospitals, circus area specially which are under the camera notification.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122640037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1109/CONFLUENCE.2016.7508042
Saloni Sharma, Sanjay Singh
This study investigates the efficiency of various models used to forecast unemployment rates. The objective of the study is to find the model which most accurately predicts the unemployment rates. It starts with auto regressive models like autoregressive moving average model and smooth transition auto regressive model and then continues to explore four types of neural networks, namely multi layer perceptron, recurrent neural network, psi sigma neural network and radial basis function neural network. In addition to these, it also uses learning vector quantization in a combination with radial basis neural network. The results have shown that the combination of learning vector quantization and radial basis function neural network outperforms all the other forecasting models. It further uses ensemble techniques like support vector regression, simple average, to give even more accurate results.
{"title":"Unemployment rates forecasting using supervised neural networks","authors":"Saloni Sharma, Sanjay Singh","doi":"10.1109/CONFLUENCE.2016.7508042","DOIUrl":"https://doi.org/10.1109/CONFLUENCE.2016.7508042","url":null,"abstract":"This study investigates the efficiency of various models used to forecast unemployment rates. The objective of the study is to find the model which most accurately predicts the unemployment rates. It starts with auto regressive models like autoregressive moving average model and smooth transition auto regressive model and then continues to explore four types of neural networks, namely multi layer perceptron, recurrent neural network, psi sigma neural network and radial basis function neural network. In addition to these, it also uses learning vector quantization in a combination with radial basis neural network. The results have shown that the combination of learning vector quantization and radial basis function neural network outperforms all the other forecasting models. It further uses ensemble techniques like support vector regression, simple average, to give even more accurate results.","PeriodicalId":299044,"journal":{"name":"2016 6th International Conference - Cloud System and Big Data Engineering (Confluence)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125974212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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